Inducing Sparsity and Shrinkage in Time-Varying Parameter Models. Issue 3 (3rd July 2021)

• Record Type: Journal Article
• Authors: Huber, Florian; Koop, Gary; Onorante, Luca
• Is Part Of: Journal of business & economic statistics. Volume 39:Issue 3(2021)
• Page Start: 669
• Page End: 683
• Publication Date: 2021-07-03
• Subjects: Hierarchical priors -- Shrinkage -- Sparsity -- Time-varying parameter regression -- Vector autoregressions; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2020.1713796
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract: Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to reduce this uncertainty and improve forecasts. In this article, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise, we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.